Random Forest Oob Score

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Out-of-bag estimates help avoid the need for an independent validation dataset, but often underestimate actual performance improvement and the optimal number of iterations.[2] See also[edit] Boosting (meta-algorithm) Bootstrapping (statistics) Cross-validation (statistics) Depending on your needs, i.e., better precision (reduce false positives) or better sensitivity (reduce false negatives) you may prefer a different cutoff. Springer. McCoy, decoy, and coy Take a ride on the Reading, If you pass Go, collect $200 Factorising Indices more hot questions question feed default about us tour help blog chat data http://fasterdic.com/out-of/oob-error-random-forest-r.html

share|improve this answer answered Jun 19 '12 at 22:15 Matt Krause 10.5k12158 Randomly selecting from the dominant class sounds reasonable. will you please give me some resources to find a bit detail about the plot you suggested. Check out the strata argument. I don't know if there's literature on how to choose an optimally representative subset (maybe someone else can weigh in?), but you could start by dropping examples at random.

Random Forest Oob Score

of variables tried at each split: 3 OOB estimate of error rate: 6.8% Confusion matrix: 0 1 class.error 0 5476 16 0.002913328 1 386 30 0.927884615 > nrow(trainset) [1] 5908 r You should try balancing your set either by sampling the "0" class only to have about the same size as "1" class or by playing with classwt parameter. What is the difference (if any) between "not true" and "false"? In it, you'll get: The week's top questions and answers Important community announcements Questions that need answers see an example newsletter By subscribing, you agree to the privacy policy and terms

What game is this picture showing a character wearing a red bird costume from? "Have permission" vs "have a permission" can i cut a 6 week old babies fingernails How to Why isn't tungsten used in supersonic aircraft? Linked 3 ROC vs Accuracy Related 11Why does the random forest OOB estimate of error improve when the number of features selected are decreased?1random forest classification in R - no separation Out Of Bag Estimation Breiman Privacy policy About Wikipedia Disclaimers Contact Wikipedia Developers Cookie statement Mobile view current community blog chat Cross Validated Cross Validated Meta your communities Sign up or

Adjust your loss function/class weights to compensate for the disproportionate number of Class0. SIM tool error installing new sitecore instance can phone services be affected by ddos attacks? They don't need to be equal: even a 1:5 ratio should be an improvement. –Itamar Jun 20 '12 at 11:35 @Itmar,that's definitely what I would try first. This computer science article is a stub.

Out Of Bag Prediction

You can help Wikipedia by expanding it. It might make sense to try Class0 = 1/0.07 ~= 14x Class1 to start, but you may want to adjust this based on your business demands (how much worse is one Random Forest Oob Score pp.316–321. ^ Ridgeway, Greg (2007). Out Of Bag Error Cross Validation You've got a few options: Discard Class0 examples until you have roughly balanced classes.

Text is available under the Creative Commons Attribution-ShareAlike License; additional terms may apply. We are trying to predict voluntary separations. The OOB is 6.8% which I think is good but the confusion matrix seems to tell a different story for predicting terms since the error rate is quite high at 92.79% Fill in the Minesweeper clues How to prove that a paper published with a particular English transliteration of my Russian name is mine? Out Of Bag Typing Test

An Introduction to Statistical Learning. Have you used it before? Browse other questions tagged r classification error random-forest or ask your own question. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the

I tried it with different values but got identical results to the default classwt=NULL. –Zhubarb Sep 23 '15 at 7:38 add a comment| up vote 5 down vote Based on your Breiman [1996b] You can pass a subset argument to randomForest, which should make this trivial to test. up vote 28 down vote favorite 20 I got a an R script from someone to run a random forest model.

predicts well only the bigger class).

Asking for a written form filled in ALL CAPS Interviewee offered code samples from current employer -- should I accept? All these can be easily plotted using the 2 following functions from the ROCR R library (available also on CRAN): pred.obj

How to replace words in more than one line in the vi editor? Wikipedia® is a registered trademark of the Wikimedia Foundation, Inc., a non-profit organization. Note that your overall error rate is ~7%, which is quite close to the percent of Class1 examples! It's possible that some of your trees were trained on only Class0 data, which will obviously bode poorly for their generalization performance.

or will write few sentences about how to interpret it. However, it seems like there must be some way to ensure that the examples you retain are representative of the larger data set. –Matt Krause Jun 28 '12 at 1:01 1 FOREST_model